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2Segment-based musculoskeletal models allow the prediction of muscle, ligament and joint forces without making assumptions regarding joint degrees of freedom. The 4 dataset published for the "Grand Challenge Competition to Predict In Vivo Knee Loads" provides directly-measured tibiofemoral contact forces for activities of daily living. For 6 the "Sixth Grand Challenge Competition to Predict In Vivo Knee Loads", blinded results for "smooth" and "bouncy" gait trials were predicted using a customised patient-specific 8 musculoskeletal model. For an unblinded comparison the following modifications were made to improve the predictions: 10  further customisations, including modifications to the knee centre of rotation;  reductions to the maximum allowable muscle forces to represent known loss of 12 strength in knee arthroplasty patients; and  a kinematic constraint to the hip joint to address the sensitivity of the segment-14 based approach to motion tracking artefact.For validation, the improved model was applied to normal gait, squat and sit-to-stand 16 for three subjects. Comparisons of the predictions with measured contact forces showed that segment-based musculoskeletal models using patient-specific input data 18 can estimate tibiofemoral contact forces with root mean square errors (RMSEs)

Accurate muscle geometry for musculoskeletal models is important to enable accurate subject-specific simulations. Commonly, linear scaling is used to obtain individualised muscle geometry. More advanced methods include non-linear scaling using segmented bone surfaces and manual or semi-automatic digitisation of muscle paths from medical images. In this study, a new scaling method combining non-linear scaling with reconstructions of bone surfaces using statistical shape modelling is presented. Statistical Shape Models (SSMs) of femur and tibia/fibula were used to reconstruct bone surfaces of nine subjects. Reference models were created by morphing manually digitised muscle paths to mean shapes of the SSMs using non-linear transformations and inter-subject variability was calculated. Subject-specific models of muscle attachment and via points were created from three reference models. The accuracy was evaluated by calculating the differences between the scaled and manually digitised models. The points defining the muscle paths showed large inter-subject variability at the thigh and shank - up to 26mm; this was found to limit the accuracy of all studied scaling methods. Errors for the subject-specific muscle point reconstructions of the thigh could be decreased by 9% to 20% by using the non-linear scaling compared to a typical linear scaling method. We conclude that the proposed non-linear scaling method is more accurate than linear scaling methods. Thus, when combined with the ability to reconstruct bone surfaces from incomplete or scattered geometry data using statistical shape models our proposed method is an alternative to linear scaling methods.

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This study demonstrates substantial loads through the glenohumeral joint during activities of daily living. The ratios of glenohumeral shear force component to compression force component are considerable when high loads act at long lever arms and at high angles of arm elevation. These glenohumeral ratios represent a key component of loading that should be considered when designing implants, surgical procedures, or rehabilitation protocols.

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Aims Patients with recurrent anterior dislocation of the shoulder commonly have an anterior osseous defect of the glenoid. Once the defect reaches a critical size, stability may be restored by bone grafting. The critical size of this defect under non-physiological loading conditions has previously been identified as 20% of the length of the glenoid. As the stability of the shoulder is load-dependent, with higher joint forces leading to a loss of stability, the aim of this study was to determine the critical size of an osseous defect that leads to further anterior instability of the shoulder under physiological loading despite a Bankart repair. Patients and Methods Two finite element (FE) models were used to determine the risk of dislocation of the shoulder during 30 activities of daily living (ADLs) for the intact glenoid and after creating anterior osseous defects of increasing magnitudes. A Bankart repair was simulated for each size of defect, and the shoulder was tested under loading conditions that replicate in vivo forces during these ADLs. The critical size of a defect was defined as the smallest osseous defect that leads to dislocation. Results The FE models showed a high risk of dislocation during ADLs after a Bankart repair for anterior defects corresponding to 16% of the length of the glenoid. Conclusion This computational study suggests that bone grafting should be undertaken for an anterior osseous defect in the glenoid of more than 16% of its length rather than a solely soft-tissue procedure, in order to optimize stability by restoring the concavity of the glenoid.

Linear scaling of generic shoulder models leads to substantial errors in model predictions. Customisation of shoulder modelling through magnetic resonance imaging (MRI) improves modelling outcomes, but model development is time and technology intensive. This study aims to validate 10 MRI-based shoulder models, identify the best combinations of anthropometric parameters for model scaling, and quantify the improvement in model predictions of glenohumeral loading through anthropometric scaling from this anatomical atlas. The shoulder anatomy was modelled using a validated musculoskeletal model (UKNSM). Ten subject-specific models were developed through manual digitisation of model parameters from high-resolution MRI. Kinematic data of 16 functional daily activities were collected using a 10-camera optical motion capture system. Subject-specific model predictions were validated with measured muscle activations. The MRI-based shoulder models show good agreement with measured muscle activations. A tenfold cross-validation using the validated personalised shoulder models demonstrates that linear scaling of anthropometric datasets with the most similar ratio of body height to shoulder width and from the same gender (p < 0.04) yields best modelling outcomes in glenohumeral loading. The improvement in model reliability is significant (p < 0.02) when compared to the linearly scaled-generic UKNSM. This study may facilitate the clinical application of musculoskeletal shoulder modelling to aid surgical decision-making.